A monitoring vehicle has a side view mirror for each side thereof and a rear-view mirror at the front center of its cabin for a good field of view of the side and the rear needed for change of lanes by a driver.
Although the side view mirror is used for seeing each side and the rear thereof, it has a blind spot (BS) where the driver cannot see a monitored vehicle or any other objects that are very close thereto.
This has been a problem because there can be an accident with the monitored vehicle in the blind spot if the driver changes lanes without seeing the monitored vehicle.
To prevent such a problem, the driver sometimes put a convex mirror onto a corner of the side view mirror, which enables the driver to see the blind spot.
However, even when the convex mirror is added onto the side view mirror, the driver must see the blind spot with his/her own eyes to change lanes which puts further strain to the driver, and there may exist the blind spot that still cannot be seen through the convex mirror even if the driver alters his/her head position.
To prevent this, a blind spot monitoring system is suggested recently that aims to prevent accidents from happening when the driver changes lanes without noticing the monitored vehicle in the blind spot, by providing the driver with information on a detection of the monitored vehicle, that is located in the blind spot or approaching the blind spot, through a sensor placed at the rear of the monitoring vehicle.
Especially, blind spot monitoring systems using a vision sensor generally adopt algorithms capable of detecting several characteristics based on visual information.
However, those algorithms may show limited detection rates constrained by external environment, shape of the objects, and a configuration of a system. Because an accurate detection requires a number of visual processing, a computational load is very heavy. Therefore, real-time detection may be difficult in an embedded system due to limited processing resources.
One of the major issues in a convolutional neural network (CNN) that causes a slow speed is a region proposal network (RPN). To extract a candidate from a final feature map, the RPN determines whether a sliding window includes a candidate in each and every location. A fully connected (FC) determines whether the candidate is a vehicle, however, many of the candidates overlap each other and the RPN consumes much running time performing computation on these redundant candidates which hardly help improve a detection rate.
As another example of detecting the monitored vehicle using a vision sensor, there is an optical flow method which expresses movement of a visual pixel by a motion vector. However, an algorithm for recognition of the monitored vehicle using the optical flow method has much dependence on a change of a state of a background and a visual noise, and requires an enormous computational load, therefore, real-time detection of the monitored vehicle is not easy.